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Last probed Nov 3, 2026 · ambodi.com
21tools discovered
For AI agents — MCPBundles Directory API
For MCP client apps (human connect):
MCP connect URL: https://ambodi.com/mcp
Skill page: https://www.mcpbundles.com/skills/ambodi-mcp-c6b279aecb
Explore capabilities (21)
read 21
Files
4
Get
3
Run Skill
2
Workspace
2
Other capabilities
10
Run Skill
read
run_skill
Execute a health skill deterministically, self-fetching all context data.
Loads the named skill, fetches the user's latest indicator values for
all LOINC codes referenced in the skill's triggers and recall sections,
builds the expression context from DB data + patient profile, and runs
the full SkillEngine pipeline.
Available skills:
- ``"cgm_glucose_management"`` — **CGM blood glucose analysis**.
Reads 5-min Yuwell CGM data from series_data, runs
biorhythm pipeline (meal detection, parameter ex
Description may be truncated on the public preview.
Run Skill — Execute a health skill deterministically, self-fetching all context data.
Loads the named skill, fetches the user's latest indicator values for
all LOINC codes referenced in the skill's triggers and recall sections,
builds the expression context from DB data + patient profile, and runs
the full SkillEngine pipeline.
Available skills:
- ``"cgm_glucose_management"`` — **CGM blood glucose analysis**.
Reads 5-min Yuwell CGM data from series_data, runs
biorhythm pipeline (meal detection, parameter ex
List Skill — List dynamically registered skills available to the current user.
Read Skill Document — Read the full document for a dynamically registered skill.
Execute Python Code — Execute Python code in sandbox with file access.
The code runs in an isolated sandbox with access to uploaded files.
Inside the sandbox:
- read input files from /home/user/input/
- write generated files to /home/user/output/
Files in /home/user/output/ matching the output patterns will be
downloaded back to the workspace and appear under /workspace/output/.
/workspace/output is the published workspace path after execution, not
a writable sandbox path, so do not write to it from sandbox code.
Run Skill Script — Run a skill script in sandbox with automatic file sync.
This tool executes Python scripts from the skills directory in an isolated
sandbox. Input files are automatically uploaded, and output files are
downloaded back to the workspace.
Workspace Execute — Execute Python code in the sandbox using session workspace paths and/or file_ids.
Workspace List — List files in workspace directory.
Discover — Return a complete health overview for the current user in one call.
This is the recommended first tool call for any conversation. It returns:
- **patient**: demographics (name, gender, birth), total indicators, and data span
- **skills_available**: applicable health assessment skills
- **data_gaps**: missing indicators for skill evaluations
Use ``query`` to filter results to a specific topic (e.g. "liver",
"blood pressure", "diabetes"). Without query, returns the full overview.
After discover, u
Get your MCP into directories
A working endpoint is step one. Directory coverage is the coordinated launch across ChatGPT, Claude, Cursor, the MCP Registry, and community indexes.
Ambodi Drive is an MCP server at ambodi.com. MCPBundles discovered 21 tools on the live endpoint.
How do I connect Ambodi Drive to my AI agent?
Use the MCP endpoint listed on this page in your MCP client configuration. One-click install pills support Claude, Cursor, VS Code, and other hosts. Copy the remote MCP URL if your client needs a manual entry.
How many tools does Ambodi Drive provide?
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Query Data — Execute a read-only SQL query on the user's health data.
**CGM / continuous glucose data**: Do NOT use query_data for CGM blood
glucose analysis. CGM readings are in series_data, not in _user_data
(th_series_data). Use ``run_skill(skill_name="cgm_glucose_management")`` instead.
Use this tool when you need full SQL expressiveness: aggregation,
window functions, CTEs, subqueries, CASE expressions, etc.
The query runs directly on the database — no pagination needed.
**Table: _user_data** (pre-filte
Compute — Run pandas/numpy code on an indicator's time-series data.
The code runs in a sandbox with pre-loaded variables:
- ``df`` — DataFrame with columns (date, value) for the primary indicator.
- ``pd``, ``np`` — pandas and numpy.
- ``fetch(name)`` — load another indicator → DataFrame(date, value).
- ``fetch_events(indicator_name)`` — events affecting indicator →
DataFrame(event_name, start, end, duration_days, before_mean, during_mean, pct_change, change).
- ``fetch_indicators(event_name)`` — indicato
Files Attach — Attach original files (PDF, image) for native model inspection.
Data Surface: `native_source`
Sends the raw file bytes to the model's vision/document understanding
capability. Accepts any file_id: single file, folder parent, or folder
child. For folder parents, attaches ALL child files for native
inspection. For individual files or children, attaches that single file.
Use when: parsed text is incomplete or garbled, file parse failed,
user asks to see the original document, or you need to verify
Files List — List the user's uploaded files with metadata and summaries.
Data Surface: `file_inventory`
Returns top-level files only (not folder children). Each entry includes
file_id, name, type, upload date, parse_status, and content_summary.
Folder entries additionally include a children array with each child's
file_id, name, and summary.
This tool returns metadata and summaries, NOT file contents.
To see the structured indicator data from a file, use
``get_data(source="<file_id>")`` or ``get_data(source=
Files Read — Read the parsed text content of a file by file_id.
Data Surface: `parsed_narrative`
Accepts any file_id: top-level single file, folder parent, or folder
child. For folder parents, returns the merged full parsed text. For
folder children, returns that child's individual parsed text.
Useful when the user asks about overall file content, wants a summary,
or needs context beyond structured indicators (doctor's notes,
diagnostic impressions, textual findings).
Depends on parse quality. Does NOT retur
Files Write
Get Health Observations — Return the patient's chat-extracted narrative health observations,
most recent first, optionally filtered to one `category`
(symptom / diet_note / exercise / sleep / condition_change /
feeling / other).
Relate — Find data-driven associations between events and indicators.
Statistical before/during comparison on actual time-series data.
Supports multi-entity queries with built-in intersection.
Directions:
- ``scope="event"``: given indicator names → find events that significantly changed them.
- ``scope="indicator"``: given event keywords → find indicators significantly changed.
Operations:
- ``operation="any"`` (default): union — items related to ANY entity.
- ``operation="all"``: intersection — items r
Ambodi Search Medical Literature — [Coarse Search] Batch search for medical literature with pagination, used for preliminary filtering.
Returns basic metadata of multiple articles (title, abstract, authors, affiliations, etc.), WITHOUT full text.
Use case: Discover relevant literature by keywords, perform initial screening based on abstracts and metadata.
To get the complete full text of a specific article, use get_article_by_pmcid() for precise retrieval.
Get Article By Pmcid — [Precise Query] Retrieve complete information for a single article by PMCID, INCLUDING full text.
Used for in-depth analysis of articles filtered from search_medical_literature() coarse search.
Use case: Read full paper content, extract detailed information such as study methods, results, conclusions for fine-grained screening.
Typical workflow:
1. Use search_medical_literature() to search for relevant literature, get article list
2. Perform initial filtering based on title, abstract, keywords,
Get Data — Retrieve health indicator data with composable query dimensions.
**CGM / continuous glucose data**: Do NOT use get_data for CGM blood
glucose analysis. CGM readings are in series_data (not th_series_data).
Use ``run_skill(skill_name="cgm_glucose_management", params={"start_time": "...", "end_time": "..."})``
instead — it runs the full biorhythm analysis pipeline (meal detection,
parameter extraction, system diagnosis) on the CGM data.
Three query dimensions (at least one required):
- **concepts*
Report Timeline — Report-anchored indicator time-series for report-parsed (lab/exam) indicators.
Returns the user's report indicators within the last `years` (default 3), anchored
at the user's MOST RECENT report (NOT today). Each row's `time` is its source
report's `report_date` (YYYY-MM-DD). USE THIS for report-indicator trends over
time — `get_data`'s start_time is unreliable for report rows (a write-back
`now()` fallback stamps undated indicators with the parse date). Device/CGM rows
and undated reports are e
Cgm Meal Report — Return a Markdown CGM meal-segment parameter report for a file.
Reads persisted ``cgm_*`` rows from ``th_series_data`` and assembles
them into a meal-first Markdown report with parameter tables and
quality flags. Optionally filters meal windows by ``start_time`` /
``end_time`` (UTC ISO date or datetime strings).
MCPBundles probed 21 tools on the live server. The tool list on this page reflects what was discovered at the last refresh — connect your client to see the full set available to your session.
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